1. Field of the Invention
The present invention relates generally to the field of forecasting uncertain future events, and more particularly to a system and method for representing and incorporating available information into uncertainty-based forecasts.
2. Description of Related Art
Many important economic variables, such as demand, supply, and price of materials and products, evolve over time in a way that cannot be perfectly predicted, but about which relevant information is available. This information may take the form of relevant historical data and expert opinion (frequently based on or augmented by various forms of analysis and modeling).
For example, economic variables commonly exhibit trends or cycles over time, driven by factors including temporal or seasonal patterns in supply or demand, product or technology lifecycles, and cycles or trends in relevant competitive or economic environment. In most such cases, relevant but incomplete information about the characteristics of these trends or cycles (e.g., distribution of their start time, size, shape, and duration and their variability over time) is available. The nature and extend of this information defines a level of uncertainty about future values of the variable or variables of interest.
To make decisions which depend on or relate to the future value of such uncertain variables effectively, it is vital to be able to effectively utilize all available information about likely future values of the variables. For decision-making purposes, information of this kind is typically captured in the form of forecasts of the relevant variable(s). The forecast constructed may be deterministic, representing a single “best guess” of the values of the variable(s) over future periods, or may be uncertain and comprised of multiple prospective sets of values over future periods, with each associated “scenario” or sample path of possible events over time assigned a relative likelihood of occurrence.
The later type of forecast (hereinafter referred to as “uncertainty-based forecast) is able to incorporate significantly more information than a deterministic forecast, since the uncertainty-based forecast is able to reflect, among other things, available information about 1) a range of outcomes that may occur at each future point in time and their relative likelihood and 2) relative likelihood of future values of the variable(s) of interest at future points in time conditional on their actual values prior to that time, as well as other data and expert opinion and analysis that will be available at that time.
While an ability to capture this wide range of information is an important benefit of uncertain-based forecasts, in order for this benefit to be realized, efficient methods of incorporating all such relevant information into an uncertainty-based forecast, and of efficiently updating and revising this uncertainty-based forecast as new information becomes available, are required. Disadvantageously, prior art systems and methods are unable to accurately or efficiently represent uncertainty-based forecasts. Furthermore, prior art systems and methods are unable to efficiently and effectively incorporate available information form diverse sources into uncertainty-based forecasts, including both directly relevant historical data and expert opinion and analysis.
One method of representing uncertainty-based forecasts is through the use of a mathematical or statistical model. A benefit of representing the uncertainty-based forecast with a model is the relative ease of working with the model in contrast to working directly with individual values of an uncertainty-based forecast that is represented, for example, by exhaustive enumeration or in uncertainty tree form (i.e., forms that require direct interaction with and manipulation of large amounts of data). A mathematical or statistical representation of the uncertainty-based forecast is generally comprised of only a model structure and a set of values for the associated parameters of the model, which together may be used to generate a representation of the uncertainty-based forecast as required.
Key challenges associated with using models to represent uncertainty-based forecasts include identification or construction of the model structure capable of accurately representing the uncertainty-based forecast of interest, and determination of the parameter values of the selected model so that the representation of the uncertainty-based forecast generated by the model accurately reflects all available information about the uncertainty-based forecast. Thus, the use of a model to represent a particular uncertainty-based forecast, while potentially powerful, is only practical if both of these challenges can be overcome.
Therefore, there is a need for a system and method for forecasting uncertain future events by representing and incorporating available information into uncertainty-based forecasts.